Data & Analytics Architect – Snowflake
Role Overview
The organization is initiating a data-platform modernization program for its Commercial Real Estate (CRE) analytics ecosystem. The engagement begins with a one-month discovery and assessment phase to evaluate existing systems, identify gaps, and define a future-state data platform on Snowflake .
The Data & Analytics Architect will play a key leadership role by conducting technical discovery, guiding architecture decisions, and producing a clear modernization roadmap that incorporates enterprise data management and observability capabilities .
Key Responsibilities
Lead technical and business discovery sessions to understand current data sources, workflows, and reporting processes
Assess the current-state data architecture , including ingestion, transformation, storage, analytics, and data management practices
Evaluate existing Data Quality (DQ), Data Governance (DG), Master/Reference Data Management (MDM), and Data Observability (DO) maturity
Identify gaps related to scalability, data quality, governance, lineage, monitoring, and manual processes
Design the future-state Snowflake-based data architecture , including Bronze/Silver/Gold layers
Define how DQ rules, DG policies, MDM integration, and observability frameworks integrate with the Snowflake platform
Recommend modernization strategies for legacy systems, spreadsheets, and siloed reporting
Translate business requirements into clear architectural recommendations and trade-offs
Produce key discovery deliverables such as:
Current-state assessment
Gap analysis (architecture + data management)
Future-state architecture diagrams
High-level modernization roadmap
Required Qualifications:
10+ years of experience in data architecture, data engineering, or analytics architecture
Strong hands-on experience with Snowflake (data modeling, performance, security, cost optimization)
Proven experience leading discovery or assessment phases for data modernization initiatives
Solid understanding of enterprise data management concepts , including:
Data Quality (DQ)
Data Governance (DG) and stewardship models
Master and Reference Data Management (MDM)
Data Observability (monitoring, alerting, anomaly detection)
Strong knowledge of ETL/ELT patterns, cloud data platforms, and analytical data modeling
Ability to operate in ambiguous environments and drive clarity through structured assessment
Strong stakeholder communication and documentation skills
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